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Merge pull request #3223 from vbystricky:oclopt_BgSubMOG2
This commit is contained in:
Alexander Alekhin
commit
7e8846b81e
@ -188,10 +188,11 @@ public:
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int nchannels = CV_MAT_CN(frameType);
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CV_Assert( nchannels <= CV_CN_MAX );
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CV_Assert( nmixtures <= 255);
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if (ocl::useOpenCL() && opencl_ON)
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{
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kernel_apply.create("mog2_kernel", ocl::video::bgfg_mog2_oclsrc, format("-D CN=%d -D NMIXTURES=%d", nchannels, nmixtures));
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create_ocl_apply_kernel();
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kernel_getBg.create("getBackgroundImage2_kernel", ocl::video::bgfg_mog2_oclsrc, format( "-D CN=%d -D NMIXTURES=%d", nchannels, nmixtures));
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if (kernel_apply.empty() || kernel_getBg.empty())
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@ -213,7 +214,7 @@ public:
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u_mean.setTo(Scalar::all(0));
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//make the array for keeping track of the used modes per pixel - all zeros at start
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u_bgmodelUsedModes.create(frameSize, CV_32FC1);
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u_bgmodelUsedModes.create(frameSize, CV_8UC1);
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u_bgmodelUsedModes.setTo(cv::Scalar::all(0));
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}
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else
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@ -259,7 +260,17 @@ public:
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virtual void setComplexityReductionThreshold(double ct) { fCT = (float)ct; }
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virtual bool getDetectShadows() const { return bShadowDetection; }
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virtual void setDetectShadows(bool detectshadows) { bShadowDetection = detectshadows; }
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virtual void setDetectShadows(bool detectshadows)
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{
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if ((bShadowDetection && detectshadows) || (!bShadowDetection && !detectshadows))
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return;
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bShadowDetection = detectshadows;
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if (!kernel_apply.empty())
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{
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create_ocl_apply_kernel();
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CV_Assert( !kernel_apply.empty() );
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}
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}
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virtual int getShadowValue() const { return nShadowDetection; }
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virtual void setShadowValue(int value) { nShadowDetection = (uchar)value; }
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@ -372,6 +383,7 @@ protected:
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bool ocl_getBackgroundImage(OutputArray backgroundImage) const;
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bool ocl_apply(InputArray _image, OutputArray _fgmask, double learningRate=-1);
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void create_ocl_apply_kernel();
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};
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struct GaussBGStatModel2Params
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@ -745,16 +757,11 @@ bool BackgroundSubtractorMOG2Impl::ocl_apply(InputArray _image, OutputArray _fgm
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learningRate = learningRate >= 0 && nframes > 1 ? learningRate : 1./std::min( 2*nframes, history );
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CV_Assert(learningRate >= 0);
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UMat fgmask(_image.size(), CV_32SC1);
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fgmask.setTo(cv::Scalar::all(1));
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_fgmask.create(_image.size(), CV_8U);
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UMat fgmask = _fgmask.getUMat();
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const double alpha1 = 1.0f - learningRate;
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int detectShadows_flag = 0;
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if(bShadowDetection)
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detectShadows_flag = 1;
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UMat frame = _image.getUMat();
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float varMax = MAX(fVarMin, fVarMax);
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@ -762,16 +769,15 @@ bool BackgroundSubtractorMOG2Impl::ocl_apply(InputArray _image, OutputArray _fgm
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int idxArg = 0;
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadOnly(frame));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadWriteNoSize(u_bgmodelUsedModes));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadWriteNoSize(u_weight));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadWriteNoSize(u_mean));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadWriteNoSize(u_variance));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::PtrReadWrite(u_bgmodelUsedModes));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::PtrReadWrite(u_weight));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::PtrReadWrite(u_mean));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::PtrReadWrite(u_variance));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::WriteOnlyNoSize(fgmask));
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idxArg = kernel_apply.set(idxArg, (float)learningRate); //alphaT
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idxArg = kernel_apply.set(idxArg, (float)alpha1);
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idxArg = kernel_apply.set(idxArg, (float)(-learningRate*fCT)); //prune
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idxArg = kernel_apply.set(idxArg, detectShadows_flag);
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idxArg = kernel_apply.set(idxArg, (float)varThreshold); //c_Tb
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idxArg = kernel_apply.set(idxArg, backgroundRatio); //c_TB
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@ -780,18 +786,11 @@ bool BackgroundSubtractorMOG2Impl::ocl_apply(InputArray _image, OutputArray _fgm
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idxArg = kernel_apply.set(idxArg, varMax);
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idxArg = kernel_apply.set(idxArg, fVarInit);
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idxArg = kernel_apply.set(idxArg, fTau);
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kernel_apply.set(idxArg, nShadowDetection);
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if (bShadowDetection)
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kernel_apply.set(idxArg, nShadowDetection);
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size_t globalsize[] = {frame.cols, frame.rows, 1};
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if (!(kernel_apply.run(2, globalsize, NULL, true)))
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return false;
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_fgmask.create(_image.size(),CV_8U);
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UMat temp = _fgmask.getUMat();
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fgmask.convertTo(temp, CV_8U);
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return true;
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return kernel_apply.run(2, globalsize, NULL, true);
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}
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bool BackgroundSubtractorMOG2Impl::ocl_getBackgroundImage(OutputArray _backgroundImage) const
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@ -802,10 +801,10 @@ bool BackgroundSubtractorMOG2Impl::ocl_getBackgroundImage(OutputArray _backgroun
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UMat dst = _backgroundImage.getUMat();
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int idxArg = 0;
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idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::ReadOnly(u_bgmodelUsedModes));
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idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::ReadOnlyNoSize(u_weight));
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idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::ReadOnlyNoSize(u_mean));
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idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::WriteOnlyNoSize(dst));
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idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::PtrReadOnly(u_bgmodelUsedModes));
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idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::PtrReadOnly(u_weight));
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idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::PtrReadOnly(u_mean));
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idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::WriteOnly(dst));
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kernel_getBg.set(idxArg, backgroundRatio);
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size_t globalsize[2] = {u_bgmodelUsedModes.cols, u_bgmodelUsedModes.rows};
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@ -815,6 +814,13 @@ bool BackgroundSubtractorMOG2Impl::ocl_getBackgroundImage(OutputArray _backgroun
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#endif
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void BackgroundSubtractorMOG2Impl::create_ocl_apply_kernel()
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{
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int nchannels = CV_MAT_CN(frameType);
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String opts = format("-D CN=%d -D NMIXTURES=%d%s", nchannels, nmixtures, bShadowDetection ? " -D SHADOW_DETECT" : "");
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kernel_apply.create("mog2_kernel", ocl::video::bgfg_mog2_oclsrc, opts);
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}
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void BackgroundSubtractorMOG2Impl::apply(InputArray _image, OutputArray _fgmask, double learningRate)
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{
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bool needToInitialize = nframes == 0 || learningRate >= 1 || _image.size() != frameSize || _image.type() != frameType;
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@ -7,11 +7,6 @@
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#define frameToMean(a, b) (b) = *(a);
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#define meanToFrame(a, b) *b = convert_uchar_sat(a);
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inline float sqr(float val)
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{
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return val * val;
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}
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inline float sum(float val)
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{
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return val;
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@ -34,63 +29,45 @@ inline float sum(float val)
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b.z = a[2]; \
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b.w = 0.0f;
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inline float sqr(const float4 val)
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{
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return val.x * val.x + val.y * val.y + val.z * val.z;
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}
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inline float sum(const float4 val)
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{
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return (val.x + val.y + val.z);
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}
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inline void swap4(__global float4* ptr, int x, int y, int k, int rows, int ptr_step)
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{
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float4 val = ptr[(k * rows + y) * ptr_step + x];
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ptr[(k * rows + y) * ptr_step + x] = ptr[((k + 1) * rows + y) * ptr_step + x];
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ptr[((k + 1) * rows + y) * ptr_step + x] = val;
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}
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#endif
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inline void swap(__global float* ptr, int x, int y, int k, int rows, int ptr_step)
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{
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float val = ptr[(k * rows + y) * ptr_step + x];
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ptr[(k * rows + y) * ptr_step + x] = ptr[((k + 1) * rows + y) * ptr_step + x];
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ptr[((k + 1) * rows + y) * ptr_step + x] = val;
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}
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__kernel void mog2_kernel(__global const uchar* frame, int frame_step, int frame_offset, int frame_row, int frame_col, //uchar || uchar3
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__global uchar* modesUsed, int modesUsed_step, int modesUsed_offset, //int
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__global uchar* weight, int weight_step, int weight_offset, //float
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__global uchar* mean, int mean_step, int mean_offset, //T_MEAN=float || float4
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__global uchar* variance, int var_step, int var_offset, //float
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__global uchar* fgmask, int fgmask_step, int fgmask_offset, //int
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__kernel void mog2_kernel(__global const uchar* frame, int frame_step, int frame_offset, int frame_row, int frame_col, //uchar || uchar3
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__global uchar* modesUsed, //uchar
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__global uchar* weight, //float
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__global uchar* mean, //T_MEAN=float || float4
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__global uchar* variance, //float
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__global uchar* fgmask, int fgmask_step, int fgmask_offset, //uchar
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float alphaT, float alpha1, float prune,
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int detectShadows_flag,
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float c_Tb, float c_TB, float c_Tg, float c_varMin, //constants
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float c_varMax, float c_varInit, float c_tau, uchar c_shadowVal)
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float c_Tb, float c_TB, float c_Tg, float c_varMin, //constants
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float c_varMax, float c_varInit, float c_tau
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#ifdef SHADOW_DETECT
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, uchar c_shadowVal
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#endif
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)
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{
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int x = get_global_id(0);
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int y = get_global_id(1);
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weight_step/= sizeof(float);
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var_step /= sizeof(float);
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mean_step /= (sizeof(float)*cnMode);
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if( x < frame_col && y < frame_row)
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{
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__global const uchar* _frame = (frame + mad24( y, frame_step, x*CN + frame_offset));
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__global const uchar* _frame = (frame + mad24(y, frame_step, mad24(x, CN, frame_offset)));
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T_MEAN pix;
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frameToMean(_frame, pix);
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bool background = false; // true - the pixel classified as background
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uchar foreground = 255; // 0 - the pixel classified as background
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bool fitsPDF = false; //if it remains zero a new GMM mode will be added
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__global int* _modesUsed = (__global int*)(modesUsed + mad24( y, modesUsed_step, x*(int)(sizeof(int))));
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int nmodes = _modesUsed[0];
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int nNewModes = nmodes; //current number of modes in GMM
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int pt_idx = mad24(y, frame_col, x);
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int idx_step = frame_row * frame_col;
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__global uchar* _modesUsed = modesUsed + pt_idx;
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uchar nmodes = _modesUsed[0];
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float totalWeight = 0.0f;
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@ -98,114 +75,130 @@ __kernel void mog2_kernel(__global const uchar* frame, int frame_step, int frame
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__global float* _variance = (__global float*)(variance);
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__global T_MEAN* _mean = (__global T_MEAN*)(mean);
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for (int mode = 0; mode < nmodes; ++mode)
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uchar mode = 0;
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for (; mode < nmodes; ++mode)
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{
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int mode_idx = mad24(mode, idx_step, pt_idx);
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float c_weight = mad(alpha1, _weight[mode_idx], prune);
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float c_weight = alpha1 * _weight[(mode * frame_row + y) * weight_step + x] + prune;
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int swap_count = 0;
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if (!fitsPDF)
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float c_var = _variance[mode_idx];
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T_MEAN c_mean = _mean[mode_idx];
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T_MEAN diff = c_mean - pix;
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float dist2 = dot(diff, diff);
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if (totalWeight < c_TB && dist2 < c_Tb * c_var)
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foreground = 0;
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if (dist2 < c_Tg * c_var)
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{
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float c_var = _variance[(mode * frame_row + y) * var_step + x];
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fitsPDF = true;
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c_weight += alphaT;
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T_MEAN c_mean = _mean[(mode * frame_row + y) * mean_step + x];
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float k = alphaT / c_weight;
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T_MEAN mean_new = mad((T_MEAN)-k, diff, c_mean);
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float variance_new = clamp(mad(k, (dist2 - c_var), c_var), c_varMin, c_varMax);
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T_MEAN diff = c_mean - pix;
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float dist2 = sqr(diff);
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if (totalWeight < c_TB && dist2 < c_Tb * c_var)
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background = true;
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if (dist2 < c_Tg * c_var)
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for (int i = mode; i > 0; --i)
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{
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fitsPDF = true;
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c_weight += alphaT;
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float k = alphaT / c_weight;
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int prev_idx = mode_idx - idx_step;
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if (c_weight < _weight[prev_idx])
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break;
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_mean[(mode * frame_row + y) * mean_step + x] = c_mean - k * diff;
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_weight[mode_idx] = _weight[prev_idx];
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_variance[mode_idx] = _variance[prev_idx];
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_mean[mode_idx] = _mean[prev_idx];
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float varnew = c_var + k * (dist2 - c_var);
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varnew = fmax(varnew, c_varMin);
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varnew = fmin(varnew, c_varMax);
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_variance[(mode * frame_row + y) * var_step + x] = varnew;
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for (int i = mode; i > 0; --i)
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{
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if (c_weight < _weight[((i - 1) * frame_row + y) * weight_step + x])
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break;
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swap_count++;
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swap(_weight, x, y, i - 1, frame_row, weight_step);
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swap(_variance, x, y, i - 1, frame_row, var_step);
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#if (CN==1)
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swap(_mean, x, y, i - 1, frame_row, mean_step);
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#else
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swap4(_mean, x, y, i - 1, frame_row, mean_step);
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#endif
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}
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mode_idx = prev_idx;
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}
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} // !fitsPDF
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_mean[mode_idx] = mean_new;
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_variance[mode_idx] = variance_new;
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_weight[mode_idx] = c_weight; //update weight by the calculated value
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totalWeight += c_weight;
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mode ++;
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break;
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}
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if (c_weight < -prune)
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c_weight = 0.0f;
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_weight[mode_idx] = c_weight; //update weight by the calculated value
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totalWeight += c_weight;
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}
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for (; mode < nmodes; ++mode)
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{
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int mode_idx = mad24(mode, idx_step, pt_idx);
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float c_weight = mad(alpha1, _weight[mode_idx], prune);
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if (c_weight < -prune)
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{
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c_weight = 0.0f;
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nmodes--;
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nmodes = mode;
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break;
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}
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_weight[((mode - swap_count) * frame_row + y) * weight_step + x] = c_weight; //update weight by the calculated value
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_weight[mode_idx] = c_weight; //update weight by the calculated value
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totalWeight += c_weight;
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}
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totalWeight = 1.f / totalWeight;
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for (int mode = 0; mode < nmodes; ++mode)
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_weight[(mode * frame_row + y) * weight_step + x] *= totalWeight;
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nmodes = nNewModes;
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if (0.f < totalWeight)
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{
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totalWeight = 1.f / totalWeight;
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for (int mode = 0; mode < nmodes; ++mode)
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_weight[mad24(mode, idx_step, pt_idx)] *= totalWeight;
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}
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if (!fitsPDF)
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{
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int mode = nmodes == (NMIXTURES) ? (NMIXTURES) - 1 : nmodes++;
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uchar mode = nmodes == (NMIXTURES) ? (NMIXTURES) - 1 : nmodes++;
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int mode_idx = mad24(mode, idx_step, pt_idx);
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if (nmodes == 1)
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_weight[(mode * frame_row + y) * weight_step + x] = 1.f;
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_weight[mode_idx] = 1.f;
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else
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{
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_weight[(mode * frame_row + y) * weight_step + x] = alphaT;
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_weight[mode_idx] = alphaT;
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for (int i = 0; i < nmodes - 1; ++i)
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_weight[(i * frame_row + y) * weight_step + x] *= alpha1;
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for (int i = pt_idx; i < mode_idx; i += idx_step)
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_weight[i] *= alpha1;
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}
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_mean[(mode * frame_row + y) * mean_step + x] = pix;
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_variance[(mode * frame_row + y) * var_step + x] = c_varInit;
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for (int i = nmodes - 1; i > 0; --i)
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{
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if (alphaT < _weight[((i - 1) * frame_row + y) * weight_step + x])
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int prev_idx = mode_idx - idx_step;
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if (alphaT < _weight[prev_idx])
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break;
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swap(_weight, x, y, i - 1, frame_row, weight_step);
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swap(_variance, x, y, i - 1, frame_row, var_step);
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#if (CN==1)
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swap(_mean, x, y, i - 1, frame_row, mean_step);
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#else
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swap4(_mean, x, y, i - 1, frame_row, mean_step);
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#endif
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_weight[mode_idx] = _weight[prev_idx];
|
||||
_variance[mode_idx] = _variance[prev_idx];
|
||||
_mean[mode_idx] = _mean[prev_idx];
|
||||
|
||||
mode_idx = prev_idx;
|
||||
}
|
||||
|
||||
_mean[mode_idx] = pix;
|
||||
_variance[mode_idx] = c_varInit;
|
||||
}
|
||||
|
||||
_modesUsed[0] = nmodes;
|
||||
bool isShadow = false;
|
||||
if (detectShadows_flag && !background)
|
||||
#ifdef SHADOW_DETECT
|
||||
if (foreground)
|
||||
{
|
||||
float tWeight = 0.0f;
|
||||
|
||||
for (int mode = 0; mode < nmodes; ++mode)
|
||||
for (uchar mode = 0; mode < nmodes; ++mode)
|
||||
{
|
||||
T_MEAN c_mean = _mean[(mode * frame_row + y) * mean_step + x];
|
||||
int mode_idx = mad24(mode, idx_step, pt_idx);
|
||||
T_MEAN c_mean = _mean[mode_idx];
|
||||
|
||||
T_MEAN pix_mean = pix * c_mean;
|
||||
|
||||
float numerator = sum(pix_mean);
|
||||
float denominator = sqr(c_mean);
|
||||
float denominator = dot(c_mean, c_mean);
|
||||
|
||||
if (denominator == 0)
|
||||
break;
|
||||
@ -214,60 +207,67 @@ __kernel void mog2_kernel(__global const uchar* frame, int frame_step, int frame
|
||||
{
|
||||
float a = numerator / denominator;
|
||||
|
||||
T_MEAN dD = a * c_mean - pix;
|
||||
T_MEAN dD = mad(a, c_mean, -pix);
|
||||
|
||||
if (sqr(dD) < c_Tb * _variance[(mode * frame_row + y) * var_step + x] * a * a)
|
||||
if (dot(dD, dD) < c_Tb * _variance[mode_idx] * a * a)
|
||||
{
|
||||
isShadow = true;
|
||||
foreground = c_shadowVal;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
tWeight += _weight[(mode * frame_row + y) * weight_step + x];
|
||||
tWeight += _weight[mode_idx];
|
||||
if (tWeight > c_TB)
|
||||
break;
|
||||
}
|
||||
}
|
||||
__global int* _fgmask = (__global int*)(fgmask + mad24(y, fgmask_step, x*(int)(sizeof(int)) + fgmask_offset));
|
||||
*_fgmask = background ? 0 : isShadow ? c_shadowVal : 255;
|
||||
#endif
|
||||
__global uchar* _fgmask = fgmask + mad24(y, fgmask_step, x + fgmask_offset);
|
||||
*_fgmask = (uchar)foreground;
|
||||
}
|
||||
}
|
||||
|
||||
__kernel void getBackgroundImage2_kernel(__global const uchar* modesUsed, int modesUsed_step, int modesUsed_offset, int modesUsed_row, int modesUsed_col,
|
||||
__global const uchar* weight, int weight_step, int weight_offset,
|
||||
__global const uchar* mean, int mean_step, int mean_offset,
|
||||
__global uchar* dst, int dst_step, int dst_offset,
|
||||
__kernel void getBackgroundImage2_kernel(__global const uchar* modesUsed,
|
||||
__global const uchar* weight,
|
||||
__global const uchar* mean,
|
||||
__global uchar* dst, int dst_step, int dst_offset, int dst_row, int dst_col,
|
||||
float c_TB)
|
||||
{
|
||||
int x = get_global_id(0);
|
||||
int y = get_global_id(1);
|
||||
|
||||
if(x < modesUsed_col && y < modesUsed_row)
|
||||
if(x < dst_col && y < dst_row)
|
||||
{
|
||||
__global int* _modesUsed = (__global int*)(modesUsed + mad24( y, modesUsed_step, x*(int)(sizeof(int))));
|
||||
int nmodes = _modesUsed[0];
|
||||
int pt_idx = mad24(y, dst_col, x);
|
||||
|
||||
__global const uchar* _modesUsed = modesUsed + pt_idx;
|
||||
uchar nmodes = _modesUsed[0];
|
||||
|
||||
T_MEAN meanVal = (T_MEAN)F_ZERO;
|
||||
|
||||
float totalWeight = 0.0f;
|
||||
|
||||
for (int mode = 0; mode < nmodes; ++mode)
|
||||
__global const float* _weight = (__global const float*)weight;
|
||||
__global const T_MEAN* _mean = (__global const T_MEAN*)(mean);
|
||||
int idx_step = dst_row * dst_col;
|
||||
for (uchar mode = 0; mode < nmodes; ++mode)
|
||||
{
|
||||
__global const float* _weight = (__global const float*)(weight + mad24(mode * modesUsed_row + y, weight_step, x*(int)(sizeof(float))));
|
||||
float c_weight = _weight[0];
|
||||
int mode_idx = mad24(mode, idx_step, pt_idx);
|
||||
float c_weight = _weight[mode_idx];
|
||||
T_MEAN c_mean = _mean[mode_idx];
|
||||
|
||||
__global const T_MEAN* _mean = (__global const T_MEAN*)(mean + mad24(mode * modesUsed_row + y, mean_step, x*(int)(sizeof(float))*cnMode));
|
||||
T_MEAN c_mean = _mean[0];
|
||||
meanVal = meanVal + c_weight * c_mean;
|
||||
meanVal = mad(c_weight, c_mean, meanVal);
|
||||
|
||||
totalWeight += c_weight;
|
||||
|
||||
if(totalWeight > c_TB)
|
||||
if (totalWeight > c_TB)
|
||||
break;
|
||||
}
|
||||
|
||||
meanVal = meanVal * (1.f / totalWeight);
|
||||
__global uchar* _dst = dst + y * dst_step + x*CN + dst_offset;
|
||||
if (0.f < totalWeight)
|
||||
meanVal = meanVal / totalWeight;
|
||||
else
|
||||
meanVal = (T_MEAN)(0.f);
|
||||
__global uchar* _dst = dst + mad24(y, dst_step, mad24(x, CN, dst_offset));
|
||||
meanToFrame(meanVal, _dst);
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user